In the control of complex technical systems it is often desirable that the action to be carried out on the technical systems is selected such that an advantageous desired dynamic behavior of the technical system is obtained. The dynamic behavior however cannot be predicted easily with complex technical systems, so the corresponding computer-aided prediction methods will be needed in order to estimate the future behavior of the technical system and select suitable actions for regulation or control of the technical system accordingly.
Nowadays the control of technical systems is often based on expert knowledge, i.e. the automatic regulation of the system is created on the basis of this expert knowledge. However approaches are also known in which technical systems are controlled with the aid of known methods of what is referred to as reinforcement learning. The known methods are however not generally applicable to any given technical systems and often do not supply sufficiently good results.
The control of a technical system based on modeling of the technical system with neural networks, which are learned with an error back-propagation method, is described in the document by G. Scott et al., “Refining PID Controllers Using Neural Networks”, Neural Computation, No. 4, 1992, Pages 746-757.
The use of neural networks for modeling and control of technical systems in the form of combustion systems is explained in the document by Kalogirou S. A., “Artificial intelligence for the modeling and control of combustion processes: a review”, Progress in Energy and Combustion Science, Elsevier Science Publishers, Amsterdam, NL, Vol. 29, No. 6, 1. January 2003, Pages 515-566.
The document Kumpati S. Narendra et al., “Adaptive Control Using Multiple Models”, IEEE Transactions on Automatic Control, IEEE Service Center, Los Alamitos, Calif., US, Bd. 42, Nr. 2, 1. February 1997, discloses the adaptive control of a technical system based on a plurality of models, with each model corresponding to a different environment in which the technical installation operates. Based on the plurality of models a suitable control for the technical installation is then selected.